An agent-based model for consumer-to-business electronic commerce

Abstract

Electronic commerce has changed the outlook of traditional business trading behavior. It is now common to see business-to-business (B2B), business-to-consumer (B2C) and consumer-to-consumer (C2C) commerce on the Internet. However, another type of model, consumer-to-business (C2B), is seldom found. A possible reason for this phenomenon is transaction cost; to unite a group of candidate buyers’ common needs and preferences to buy a product or service is uneasy. Difficulties arise, for example, in how to synthesize individual’s preferences into a group’s consensus, how to communicate with each other within the group, and how to collectively negotiate with a seller, etc. To establish a successful business model in the electronic market, however, these processes have to be implemented. We address these issues in this paper, and propose a Buyer Collective Purchasing (BCP) model implemented in a multi-agent framework. A prototype system, which uses a laptop computer purchasing case as an example, is created to demonstrate the idea and show how the model works.

Introduction

The explosion of Internet and the ensuing applications in electronic commerce (e-commerce) have permanently changed the outlook of traditional business trading behavior. Different business parties are now made easy to interact through Internet with others to conduct transactions in a more efficient way. Based on the nature of transactions, e-commerce is classified into following types (Turban, Lee, King, & Chung, 1999): business-to-business (B2B), business-to-consumer (B2C), consumer-to-consumer (C2C), consumer-to-business (C2B), non-business e-commerce, and intra-business e-commerce.

Compared to the three frequently mentioned models: B2B, B2C, and C2C, which are now very popular, the progress of the other one (i.e., C2B) is far left behind; it is seldom seen on the Internet. A possible reason for this situation is the high transaction cost. It takes effort to unify a group of buyers’ common needs and preferences and to interact between the buyer’s party and the potential venders in order to complete a transaction. Moreover, it is not clear how to do it; there is little research into this problem. For example, how to synthesize individual’s needs into a group’s consensus? What is the mechanism to communicate with each other within the group? How does the group collectively negotiate with a seller? All these problems need a solution if one wants to create a successful C2B trading model.

Collective purchasing is not new to the traditional business. Friends sometimes invite each other to go to a restaurant for a meal and share the expense. People join a tour to share the expense of transportation, hotels and other expenditure. In these cases, people scarify some of their personal preferences in order to gain benefits from the collectively purchasing behavior. Likewise, can we transfer such consumer behavior to the e-market? If with a suitable model and mechanism, we believe Internet will be an enabler, not an obstacle, to collective purchasing behavior because people there get easier to setup a group with common interests.

In this paper, we define and propose a model for buyer collective purchasing (BCP) behavior, which consists of a number of steps, each for a specific task, e.g., product description, buyer invitation, needs synthesis, negotiation, etc. A multi-agent architecture is devised to facilitate this job. In the framework, different agents, each assigned with a specific role, cooperate together to support the process. For example, there is an agent for each buyer who participates in collective purchasing to record the buyer’s needs and preferences. Similarly, there are agents for sellers to represent their offers to the purchase. An agent is responsible for collecting and synthesizing the buyers’ needs. Another plays the role for negotiation. Among these agents, the platform itself supports communication and interaction within the group.

Behind the multi-agent framework, there need algorithms to collect and synthesize the buyers’ preferences and to negotiate with sellers. An AHP (Analytic Hierarchy Process) algorithm is devised to synthesize the common needs from the buyer group. Based on the created AHP tree, a one-to-many negotiation algorithm takes place to seek for the best deal from potential sellers that carry products satisfying the group’s needs. Based on the proposed BCP model, we implement a prototype system to demonstrate the idea, which uses a laptop computer purchasing case to show how the model works.

The remainder of this paper is organized as follows. Section 2 discusses C2B buying behavior and defines the buyer collective purchasing model. Section 3 implements the model with a multi-agent framework. Preferences synthesis and alternatives ranking are described in Section 4, in which the algorithm of AHP used in this paper is illustrated. Section 5 describes a one-to-many negotiation algorithm that can bargain with several potential sellers simultaneously based on the information given by the constructed AHP-tree. The prototype implementation and demonstration that realize the whole idea are presented in Section 6. Finally, we conclude the paper with future research issues in Section 7.